RESULTS: All of the mutations were found in adenocarcinoma, except one that was in squamous cell carcinoma. The mutation rate was 45.7% (221/484). Complex mutations were also observed, wherein 8 tumours carried 2 mutations and 1 tumour carried 3 mutations.
CONCLUSIONS: Both methods detected EGFR mutations in FFPE samples. HRM assays gave more EGFR positive results compared to Scorpion ARMS.
METHODS: In this study, the metabolic responses of C. glabrata under acetate growth condition was explored using high-throughput transcriptomic and proteomic approaches.
RESULTS: Collectively, a total of 1482 transcripts (26.96%) and 242 proteins (24.69%) were significantly up- or down-regulated. Both transcriptome and proteome data revealed that the regulation of alternative carbon metabolism in C. glabrata resembled other fungal pathogens such as Candida albicans and Cryptococcus neoformans, with up-regulation of many proteins and transcripts from the glyoxylate cycle and gluconeogenesis, namely isocitrate lyase (ICL1), malate synthase (MLS1), phosphoenolpyruvate carboxykinase (PCK1) and fructose 1,6-biphosphatase (FBP1). In the absence of glucose, C. glabrata shifted its metabolism from glucose catabolism to anabolism of glucose intermediates from the available carbon source. This observation essentially suggests that the glyoxylate cycle and gluconeogenesis are potentially critical for the survival of phagocytosed C. glabrata within the glucose-deficient macrophages.
CONCLUSION: Here, we presented the first global metabolic responses of C. glabrata to alternative carbon source using transcriptomic and proteomic approaches. These findings implicated that reprogramming of the alternative carbon metabolism during glucose deprivation could enhance the survival and persistence of C. glabrata within the host.
METHODS: Nationally representative data of Malaysia were used to generate cross-sectional evidence. The sample size was 2156 respondents. An ordered probit regression was utilized to assess factors associated with the practice of physical activity.
RESULTS: Respondents aged 40-49 years with hypertension were 7.3% less likely to participate in high-level physical activity when compared to those without hypertension. The probability of having a low level of physical activity was 12.3% higher among hypertensive patients aged ≥60. Males, married individuals, less-educated adults, low-income earners, and individuals who were aware of their BMI, had a higher tendency to indulge in a highly active lifestyle than others.
CONCLUSION: The effect of hypertension on physical activity was moderated by age. Factors influencing physical activity levels among adults were income, gender, marital status, education, employment status, and BMI awareness.
Patients and methods: A nationally representative data of adolescents that consists of 25399 respondents is used. The demographic (age, gender, education) and lifestyle (fruits and vegetables consumption, carbonated soft drink consumption, cigarette smoking, alcohol drinking, sex behaviour, participation in physical education class, obesity) determinants of physical activity are assessed using binomial regression.
Results: The results show that age is negatively associated with time spent in physical activity. However, being male and education levels are positively related to time spent in physical activity. Having unhealthy lifestyle and being obese are associated with low levels of physical activity. Physical education seems to promote participation in physical activity.
Conclusion: In conclusion, demographic and lifestyle factors play an important role in determining levels of physical activity among adolescents. In order to reduce the prevalence of physically inactive adolescents, policy makers should focus primarily on late adolescents, females, adolescents who engage in unhealthy lifestyle and seldom attend physical education classes, as well as obese adolescents.
METHODS: Data were obtained from the Global Youth Tobacco Survey Timor-Leste. Ordered logistic regressions were used to examine the associations between knowledge of smoking and sociodemographic, and lifestyle factors. Structural equation modelling was utilized to explore the mediating effects.
RESULTS: Adolescents were less likely to have high knowledge of smoking if they were lower-secondary students, were males, had unemployed parents and had no closest friends who smoked. The relationship between grade levels and smoking knowledge was partly mediated by awareness of anti-tobacco messages on mass media, school education about the dangers of smoking and family discussion about smoking.
CONCLUSIONS: Sociodemographic and lifestyle factors play an important role in determining knowledge of smoking among adolescents. To some extent, awareness-, education- and family-related variables explain how grade levels affect smoking knowledge.